MSPA-MCS: Collaborative Research: New Methods for Robust, Feature-Preserving Surface Reconstruction

MSPA-MCS：协作研究：稳健、保留特征的表面重建的新方法

• 批准号: 0528209
• 负责人: Joseph S. Mitchell
• 金额: $ 20.51万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-15 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0528209&HistoricalAwards=false
• 关键词: MSPA; MCS; Collaborative; Research; New; MSPA; MCS; Collaborative; Research; New

Digital scanning devices are capable of acquiring high-resolution 3D models and have recently become affordable and commercially available. Modeling detailed 3D shapes by scanning real physical models is becoming commonplace. Current scanners are able to produce very large amounts of raw, dense point sets; consequently, there has been a recent increase in the need for techniques for processing point sets. One of the principal challenges faced today is the development of surface reconstruction techniques that deal with the inherent noise of the acquired dataset. When the underlying surface contains sharp features, the requirement of being resilient to noise is especially challenging, since noise and sharp features are ambiguous, and most existing techniques tend to smooth important features or even to amplify noisy samples. The project helps to train young researchers to work at the intersection of graphics, geometry, and statistics, while enabling them to pursue theoretically sound work that has deep practical impact. The data, software, and models developed in this project will be disseminated for other researchers to use in benchmarking and testing.This research involves producing efficient and theoretically sound techniques for robust, feature-preserving surface reconstruction. It builds on recent work on the construction of a manifold surface from a set of points by using a moving least-squares (MLS) technique. The project explores the use of robust statistical techniques arising from outlier identification in MLS-based surface reconstruction. The approach is related to recent developments in feature-preserving smoothing, but it defines a surface rather than filtering the geometry. The techniques not only point to more reliable MLS projection but also extend the representation power of the underlying MLS surface definition to enable the representation of objects with sharp features.

数字扫描设备能够获取高分辨率3D型号，并且最近变得负担得起且商业上可用。 通过扫描真实的物理模型对详细的3D形状进行建模变得司空见惯。 当前的扫描仪能够产生大量的原始，致密的点集；因此，最近对处理点集的技术的需求有所增加。 当今面临的主要挑战之一是发展表面重建技术，这些技术涉及所获得的数据集的固有噪声。 当基础表面包含鲜明的特征时，弹性弹性的要求特别具有挑战性，因为噪声和锋利的特征是模棱两可的，并且大多数现有技术倾向于平滑重要的特征，甚至可以放大嘈杂的样品。 该项目有助于培训年轻的研究人员在图形，几何学和统计数据的交集中工作，同时使他们能够从事具有深刻实际影响的理论上合理的工作。 该项目中开发的数据，软件和模型将被传播，以供其他研究人员用于基准测试和测试。这项研究涉及生产有效的理论上合理的技术，以实现鲁棒，具有功能的功能性，具有功能的表面重建。 它建立在最近使用移动最小二乘技术（MLS）技术从一组点构造歧管表面的工作的基础上。该项目探讨了基于MLS的表面重建中异常识别引起的强大统计技术的使用。 该方法与特征平滑的最新发展有关，但它定义了表面而不是过滤几何形状。这些技术不仅指出了更可靠的MLS投影，而且还扩展了基础MLS表面定义的表示功能，以使具有鲜明特征的对象表示。